{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The goal of this project is to predict the Crude Oil prices. Monthly petroleum prices can be found at the [Energy Information Administration](https://www.eia.gov/dnav/pet/pet_pri_spt_s1_m.htm). Ever relevant, [Wikipedia](https://en.wikipedia.org/wiki/World_oil_market_chronology_from_2003) has a great write-up on recent trends in oil prices. Also, there is this [Times](http://content.time.com/time/business/article/0,8599,1859380,00.html) article on the spike and drop in 2008.\n",
    "\n",
    "### Introduction:\n",
    "\n",
    "Tthere are different types of crude oil – the thick, unprocessed liquid that drillers extract below the earth – and some are more desirable than others. Also, where the oil comes from also makes a difference. Because of these nuances, we need benchmarks to value the commodity based on its quality and location. Thus, Brent, WTI and Dubai/Oman serve this important purpose. Here, I use the most important WTI to demonstrate the world crude oil price changes.\n",
    "\n",
    "West Texas Intermediate (WTI) – WTI refers to oil extracted from wells in the U.S. and sent via pipeline to Cushing, Oklahoma. The product itself is very light and very sweet, making it ideal for gasoline refining, in particular. WTI continues to be the main benchmark for oil consumed in the United States. Brent Blend – Roughly two-thirds of all crude contracts around the world reference Brent Blend, making it the most widely used marker of all. These days, “Brent” actually refers to oil from four different fields in the North Sea: Brent, Forties, Oseberg and Ekofisk. Crude from this region is light and sweet, making them ideal for the refining of diesel fuel, gasoline. For more information see [Wikipedia](https://en.wikipedia.org/wiki/West_Texas_Intermediate) article. \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sarthakdasadia/anaconda/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Import required packages\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "import quandl\n",
    "import math\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import sklearn.linear_model\n",
    "import sklearn.metrics\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "params = {'legend.fontsize': 'xx-large',\n",
    "          'figure.figsize': (15, 10),\n",
    "         'axes.labelsize': 'xx-large',\n",
    "         'axes.titlesize':'xx-large',\n",
    "         'xtick.labelsize':'xx-large',\n",
    "         'ytick.labelsize':'xx-large'}\n",
    "pylab.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series\n",
    "<!-- requirement: images/ts_xval.png -->\n",
    "<!-- requirement: projects/timeseries-project -->\n",
    "\n",
    "Time series prediction or forecasting presents its own challenges which are different from machine-learning problems.  \n",
    "\n",
    "Let's first define our time-series as\n",
    "\n",
    "$$ \\{X_t, t = \\ldots, -1, 0, 1, \\ldots\\} \\, . $$\n",
    "\n",
    "In general, while we could use regression to make predictions, the goal of time series analysis is to take advantage of the temporal nature of the data to make more sophisticated models.\n",
    "\n",
    "**Why not regression** : In typical time series analysis, the depended variables are missing. For exmaple, a simple linear regression would take a form of \n",
    "\n",
    "$$ Y = \\beta_0 + \\beta_1 X_1 + ...$$\n",
    "\n",
    "where X_1, X_2 ... are unknown. All we know is Y as a function of time.\n",
    "\n",
    "Here are a few concepts related to time series.  We take $\\varepsilon \\sim N(0, \\sigma^2)$ to be i.i.d. normal errors.  \n",
    "1. **Stationarity**.  Informally, this means that the distribution of the $X_t$ is independent of time $t$.  Formally, a time-series is stationary if for all $k \\ge 0$ and $t$, the following two $k$-tuples have the same distribution:\n",
    "$$ (X_0,\\ldots,X_k) \\sim (X_t,\\ldots,X_{t+k}) $$\n",
    "1. **Drift**.  One reason a time-series might not be stationary is that it possess a drift.  For example, we know that prices tend to creep up with inflation.  Mathematically, we might represent the (log) prices as\n",
    "$$ X_t = \\mu t + \\varepsilon_t $$\n",
    "1. **Seasonality**.  Another reason a time-series might not be stationary is that it posseses a seasonal component.  For example, we know that the temperature increases in the summer and decreases in the winter.  A simple model of this might be\n",
    "$$ X_t = \\alpha \\sin(\\omega t) + \\beta \\cos(\\omega t)$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Crudeoil price dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "oil = quandl.get(\"CHRIS/CME_CL1\")  # This creates pandas data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>44.89</td>\n",
       "      <td>46.35</td>\n",
       "      <td>44.88</td>\n",
       "      <td>46.33</td>\n",
       "      <td>1.11</td>\n",
       "      <td>46.04</td>\n",
       "      <td>674176.0</td>\n",
       "      <td>519676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-03</th>\n",
       "      <td>46.28</td>\n",
       "      <td>47.10</td>\n",
       "      <td>45.92</td>\n",
       "      <td>47.05</td>\n",
       "      <td>1.03</td>\n",
       "      <td>47.07</td>\n",
       "      <td>513672.0</td>\n",
       "      <td>517659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-05</th>\n",
       "      <td>47.04</td>\n",
       "      <td>47.32</td>\n",
       "      <td>44.51</td>\n",
       "      <td>45.61</td>\n",
       "      <td>1.94</td>\n",
       "      <td>45.13</td>\n",
       "      <td>1167208.0</td>\n",
       "      <td>510718.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-06</th>\n",
       "      <td>45.65</td>\n",
       "      <td>46.53</td>\n",
       "      <td>45.18</td>\n",
       "      <td>45.33</td>\n",
       "      <td>0.39</td>\n",
       "      <td>45.52</td>\n",
       "      <td>945108.0</td>\n",
       "      <td>487969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-07</th>\n",
       "      <td>45.35</td>\n",
       "      <td>45.42</td>\n",
       "      <td>43.78</td>\n",
       "      <td>44.33</td>\n",
       "      <td>1.29</td>\n",
       "      <td>44.23</td>\n",
       "      <td>958132.0</td>\n",
       "      <td>448574.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low   Last  Change  Settle     Volume  \\\n",
       "Date                                                                \n",
       "2017-06-30  44.89  46.35  44.88  46.33    1.11   46.04   674176.0   \n",
       "2017-07-03  46.28  47.10  45.92  47.05    1.03   47.07   513672.0   \n",
       "2017-07-05  47.04  47.32  44.51  45.61    1.94   45.13  1167208.0   \n",
       "2017-07-06  45.65  46.53  45.18  45.33    0.39   45.52   945108.0   \n",
       "2017-07-07  45.35  45.42  43.78  44.33    1.29   44.23   958132.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "2017-06-30                    519676.0  \n",
       "2017-07-03                    517659.0  \n",
       "2017-07-05                    510718.0  \n",
       "2017-07-06                    487969.0  \n",
       "2017-07-07                    448574.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110e39890>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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f/rLKy8uzNTwAAADkmMMRPaVz1uQRwdvtXcltyAcge7K2actPf/pTtbe3a/To0WppaQm2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7/yJ29bRKga/dJjsHrxFDw2exgAoG9qJN5+5CIAwDUT+7D7rvjedmqrTm/EXkuw8cKn+3DPK39i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\nuCrcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126ded310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oil.Open.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 29.01000023,  29.39999962,  29.29999924, ...,  47.04000092,\n",
       "        45.65000153,  45.34999847], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setting the random seed\n",
    "\n",
    "np.random.seed(7)\n",
    "dataset = oil.Open.values\n",
    "dataset = dataset.astype('float32')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sarthakdasadia/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/data.py:321: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/Users/sarthakdasadia/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/data.py:356: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.14061689,  0.14350173,  0.14276204, ...,  0.27398476,\n",
       "        0.26370293,  0.26148382], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalize the dataset\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6027, 2584)\n"
     ]
    }
   ],
   "source": [
    "# Split the datser into test and training data\n",
    "\n",
    "train_size = int(len(dataset) * 0.7)\n",
    "test_size = len(dataset) - train_size\n",
    "train, test = dataset[0:train_size], dataset[train_size:len(dataset)]\n",
    "print(len(train), len(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a simple function to convert single column of data into a two-column dataset. The first column containing today's (t) price and the second column containing next day’s (t+1) price and so on, to be predicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert an array of values into a dataset matrix\n",
    "# look_back = number of features. Use ACF and PACF to determine this\n",
    "\n",
    "def create_dataset(dataset, look_back=1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-look_back):\n",
    "        a = dataset[i:(i+look_back)]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back])\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this problem, I inspected ACF and PACF to determine look_back ~ 12."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Long Short-Term Memory (LSTM) Recurrent Network for Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Long Short-Term Memory network, or LSTM network, is a recurrent neural network that is trained using Backpropagation Through Time and overcomes the vanishing gradient problem.\n",
    "\n",
    "As such, it can be used to create large recurrent networks that in turn can be used to address difficult sequence problems in machine learning and achieve state-of-the-art results.\n",
    "\n",
    "Instead of neurons, LSTM networks have memory blocks that are connected through layers.\n",
    "\n",
    "A block has components that make it smarter than a classical neuron and a memory for recent sequences. A block contains gates that manage the block’s state and output. A block operates upon an input sequence and each gate within a block uses the sigmoid activation units to control whether they are triggered or not, making the change of state and addition of information flowing through the block conditional."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The network has a visible layer with 1 input, a hidden layer with 4 LSTM blocks or neurons, and an output layer that makes a single value prediction. The default sigmoid activation function is used for the LSTM blocks. The network is trained for 100 epochs and a batch size of 1 is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# reshape into X=t and Y=t+1\n",
    "\n",
    "look_back = 12\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "testX, testY = create_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6015, 12)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# reshape input to be [samples, time steps, features]\n",
    "\n",
    "trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
    "testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6015, 1, 12)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "30s - loss: 2.9997e-04\n",
      "Epoch 2/20\n",
      "29s - loss: 7.6833e-05\n",
      "Epoch 3/20\n",
      "29s - loss: 6.3019e-05\n",
      "Epoch 4/20\n",
      "29s - loss: 5.2739e-05\n",
      "Epoch 5/20\n",
      "29s - loss: 4.8446e-05\n",
      "Epoch 6/20\n",
      "30s - loss: 4.8699e-05\n",
      "Epoch 7/20\n",
      "29s - loss: 4.8397e-05\n",
      "Epoch 8/20\n",
      "29s - loss: 4.4940e-05\n",
      "Epoch 9/20\n",
      "29s - loss: 4.3124e-05\n",
      "Epoch 10/20\n",
      "29s - loss: 4.3059e-05\n",
      "Epoch 11/20\n",
      "31s - loss: 4.2391e-05\n",
      "Epoch 12/20\n",
      "30s - loss: 4.3402e-05\n",
      "Epoch 13/20\n",
      "29s - loss: 4.1792e-05\n",
      "Epoch 14/20\n",
      "29s - loss: 4.1057e-05\n",
      "Epoch 15/20\n",
      "29s - loss: 3.9990e-05\n",
      "Epoch 16/20\n",
      "29s - loss: 3.9415e-05\n",
      "Epoch 17/20\n",
      "29s - loss: 4.0692e-05\n",
      "Epoch 18/20\n",
      "29s - loss: 3.9444e-05\n",
      "Epoch 19/20\n",
      "29s - loss: 3.9506e-05\n",
      "Epoch 20/20\n",
      "29s - loss: 3.7066e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x12fb17d10>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create and fit the LSTM network\n",
    "\n",
    "model_LSTM = Sequential()\n",
    "model_LSTM.add(LSTM(13, input_shape=(1, look_back)))\n",
    "model_LSTM.add(Dense(1))\n",
    "model_LSTM.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model_LSTM.fit(trainX, trainY, epochs=20, batch_size=1, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make predictions\n",
    "trainPredict = model_LSTM.predict(trainX)\n",
    "testPredict = model_LSTM.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.41412175], dtype=float32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testPredict[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2572"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.80 RMSE\n",
      "Test Score: 3.36 RMSE\n"
     ]
    }
   ],
   "source": [
    "# invert predictions\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "trainY_val = scaler.inverse_transform([trainY])\n",
    "testPredict = scaler.inverse_transform(testPredict)\n",
    "testY_val = scaler.inverse_transform([testY])\n",
    "\n",
    "# calculate root mean squared error\n",
    "trainScore = math.sqrt(mean_squared_error(trainY_val[0], trainPredict[:,0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))\n",
    "testScore = math.sqrt(mean_squared_error(testY_val[0], testPredict[:,0]))\n",
    "print('Test Score: %.2f RMSE' % (testScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sarthakdasadia/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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050av8GUFPjsZfBJMX2obGCOYUeEzfTItW7btyOcqt9TX+BUeGBjx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kWlZllbPdpagvj45SuKPRSaoq5YvxAnL7Er9lrDgzAHhwAAkgFfThjwEU0ThmHZF5vS/bW/+Udb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\nYsFHREREREQUpv4C91REm0pBlHEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12fffddd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shift train predictions for plotting\n",
    "trainPredictPlot = np.empty_like(dataset)\n",
    "trainPredictPlot[:] = np.nan\n",
    "trainPredictPlot[look_back:len(trainPredict)+look_back] = trainPredict.ravel()\n",
    "\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = np.empty_like(dataset)\n",
    "testPredictPlot[:] = np.nan\n",
    "testPredictPlot[len(trainPredict)+(look_back*2):len(dataset)] = testPredict.ravel()\n",
    "\n",
    "# plot baseline and predictions\n",
    "plt.plot(scaler.inverse_transform(dataset))\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into train and test sets\n",
    "train_size = int(len(dataset) * 0.7)\n",
    "test_size = len(dataset) - train_size\n",
    "train, test = dataset[0:train_size], dataset[train_size:len(dataset)]\n",
    "\n",
    "# reshape dataset\n",
    "look_back = 12\n",
    "trainX_mpm, trainY_mpm = create_dataset(train, look_back)\n",
    "testX_mpm, testY_mpm = create_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2572, 12)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testX_mpm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "4s - loss: 9.3823e-04\n",
      "Epoch 2/50\n",
      "4s - loss: 5.4111e-05\n",
      "Epoch 3/50\n",
      "4s - loss: 5.2842e-05\n",
      "Epoch 4/50\n",
      "4s - loss: 5.1203e-05\n",
      "Epoch 5/50\n",
      "4s - loss: 4.9231e-05\n",
      "Epoch 6/50\n",
      "4s - loss: 4.5481e-05\n",
      "Epoch 7/50\n",
      "4s - loss: 4.6111e-05\n",
      "Epoch 8/50\n",
      "4s - loss: 4.3892e-05\n",
      "Epoch 9/50\n",
      "4s - loss: 4.3274e-05\n",
      "Epoch 10/50\n",
      "4s - loss: 4.4122e-05\n",
      "Epoch 11/50\n",
      "4s - loss: 4.0554e-05\n",
      "Epoch 12/50\n",
      "4s - loss: 4.1522e-05\n",
      "Epoch 13/50\n",
      "4s - loss: 3.9927e-05\n",
      "Epoch 14/50\n",
      "4s - loss: 4.0404e-05\n",
      "Epoch 15/50\n",
      "4s - loss: 3.9615e-05\n",
      "Epoch 16/50\n",
      "4s - loss: 3.8948e-05\n",
      "Epoch 17/50\n",
      "4s - loss: 3.7841e-05\n",
      "Epoch 18/50\n",
      "4s - loss: 3.8700e-05\n",
      "Epoch 19/50\n",
      "4s - loss: 3.8000e-05\n",
      "Epoch 20/50\n",
      "4s - loss: 3.6936e-05\n",
      "Epoch 21/50\n",
      "4s - loss: 3.7526e-05\n",
      "Epoch 22/50\n",
      "4s - loss: 3.7123e-05\n",
      "Epoch 23/50\n",
      "4s - loss: 3.8259e-05\n",
      "Epoch 24/50\n",
      "4s - loss: 3.7794e-05\n",
      "Epoch 25/50\n",
      "4s - loss: 3.5653e-05\n",
      "Epoch 26/50\n",
      "4s - loss: 3.4740e-05\n",
      "Epoch 27/50\n",
      "4s - loss: 3.8298e-05\n",
      "Epoch 28/50\n",
      "4s - loss: 3.8657e-05\n",
      "Epoch 29/50\n",
      "4s - loss: 3.4989e-05\n",
      "Epoch 30/50\n",
      "4s - loss: 3.5810e-05\n",
      "Epoch 31/50\n",
      "4s - loss: 3.5163e-05\n",
      "Epoch 32/50\n",
      "4s - loss: 3.6607e-05\n",
      "Epoch 33/50\n",
      "4s - loss: 3.5490e-05\n",
      "Epoch 34/50\n",
      "4s - loss: 3.5725e-05\n",
      "Epoch 35/50\n",
      "4s - loss: 3.5412e-05\n",
      "Epoch 36/50\n",
      "4s - loss: 3.5073e-05\n",
      "Epoch 37/50\n",
      "4s - loss: 3.6210e-05\n",
      "Epoch 38/50\n",
      "4s - loss: 3.5776e-05\n",
      "Epoch 39/50\n",
      "4s - loss: 3.4608e-05\n",
      "Epoch 40/50\n",
      "4s - loss: 3.4494e-05\n",
      "Epoch 41/50\n",
      "4s - loss: 3.5672e-05\n",
      "Epoch 42/50\n",
      "4s - loss: 3.6535e-05\n",
      "Epoch 43/50\n",
      "4s - loss: 3.5575e-05\n",
      "Epoch 44/50\n",
      "4s - loss: 3.5430e-05\n",
      "Epoch 45/50\n",
      "4s - loss: 3.5015e-05\n",
      "Epoch 46/50\n",
      "4s - loss: 3.5740e-05\n",
      "Epoch 47/50\n",
      "4s - loss: 3.4621e-05\n",
      "Epoch 48/50\n",
      "4s - loss: 3.3875e-05\n",
      "Epoch 49/50\n",
      "4s - loss: 3.5329e-05\n",
      "Epoch 50/50\n",
      "4s - loss: 3.3977e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x12fc9f2d0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create and fit Multilayer Perceptron model\n",
    "\n",
    "model_MPM = Sequential()\n",
    "model_MPM.add(Dense(14, input_dim=look_back, activation='relu')) # input layer with 14 neurons \n",
    "model_MPM.add(Dense(8, activation='relu')) # Second Layer\n",
    "model_MPM.add(Dense(1)) \n",
    "model_MPM.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model_MPM.fit(trainX_mpm, trainY_mpm, epochs=50, batch_size=2, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.70 RMSE\n",
      "Test Score: 1.81 RMSE\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "trainPredict_mpm = model_MPM.predict(trainX_mpm)\n",
    "testPredict_mpm = model_MPM.predict(testX_mpm)\n",
    "\n",
    "# invert predictions\n",
    "trainPredict_mpm = scaler.inverse_transform(trainPredict_mpm)\n",
    "trainY_mpm = scaler.inverse_transform([trainY_mpm])\n",
    "testPredict_mpm = scaler.inverse_transform(testPredict_mpm)\n",
    "testY_mpm = scaler.inverse_transform([testY_mpm])\n",
    "\n",
    "# calculate root mean squared error\n",
    "trainScore_mpm = math.sqrt(mean_squared_error(trainY_mpm[0], trainPredict_mpm[:,0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore_mpm))\n",
    "testScore_mpm = math.sqrt(mean_squared_error(testY_mpm[0], testPredict_mpm[:,0]))\n",
    "print('Test Score: %.2f RMSE' % (testScore_mpm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sarthakdasadia/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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0DgbDm7mEK3wkPmAiG3Yfvnw1NTXpC1/4ghobG3XaaafF2i9IUnV1taR48EsVCAQkKa1v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yWKtiSt4YuoGt7Z3wVN19EzEIR39YvonPlXvHvAesMpolnPV6Z48dML7sbdl30qT38AIip1QrFn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\nuRFLAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12fd312d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shift train predictions for plotting\n",
    "trainPredictPlot_mpm = np.empty_like(dataset)\n",
    "trainPredictPlot_mpm[:] = np.nan\n",
    "trainPredictPlot_mpm[look_back:len(trainPredict_mpm)+look_back] = trainPredict_mpm.ravel()\n",
    "\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot_mpm = np.empty_like(dataset)\n",
    "testPredictPlot_mpm[:] = np.nan\n",
    "testPredictPlot_mpm[len(trainPredict_mpm)+(look_back*2):len(dataset)] = testPredict_mpm.ravel()\n",
    "\n",
    "# plot baseline and predictions\n",
    "plt.plot(scaler.inverse_transform(dataset))\n",
    "plt.plot(trainPredictPlot_mpm)\n",
    "plt.plot(testPredictPlot_mpm)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 43.380002  ,  42.5500007 ,  42.79999867,  43.15999976,\n",
       "        43.4900011 ,  43.69999939,  44.88999775,  44.88999775,\n",
       "        46.27999851,  47.04000038,  45.64999962,  45.34999803])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testY_mpm[0][-12:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.reshape(testY_mpm[0][-12:],(1,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 43.380002  ,  42.5500007 ,  42.79999867,  43.15999976,\n",
       "         43.4900011 ,  43.69999939,  44.88999775,  44.88999775,\n",
       "         46.27999851,  47.04000038,  45.64999962,  45.34999803]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 43.44081116]], dtype=float32)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_MPM.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
